def ang_cross_corr_from_coords(ras,
decs,
refras,
refdecs,
randras,
randdecs,
minscale,
maxscale,
weights=None,
refweights=None,
randweights=None,
nthreads=1,
nbins=10):
# set up logarithimically spaced bins in units of degrees
bins = np.logspace(minscale, maxscale, (nbins + 1))
# count pairs between sample and control sample
DD_counts = DDtheta_mocks(0,
nthreads,
bins,
ras,
decs,
RA2=refras,
DEC2=refdecs,
weights1=weights,
weights2=refweights)
# extract number counts
dd = []
for j in range(nbins):
dd.append(DD_counts[j][3])
# cross correlation between sample and random catalog
DR_counts = np.array(
DDtheta_mocks(0,
nthreads,
bins,
ras,
decs,
RA2=randras,
DEC2=randdecs,
weights1=weights,
weights2=randweights))
dr = []
for j in range(nbins):
dr.append(DR_counts[j][3])
wtheta = np.array(dd) / np.array(dr) * (float(len(randras))) / float(
len(refras)) - 1
return wtheta
def angular_correlation_function(table_1, table_2, table_r, theta_bins):
n_1 = len(table_1)
n_2 = len(table_2)
n_r = len(table_r)
n = multiprocessing.cpu_count()
d1d2 = DDtheta_mocks(
False, n, theta_bins, table_1['ra'], table_1['dec'], RA2=table_2['ra'],
DEC2=table_2['dec'])['npairs']
d1r = DDtheta_mocks(
False, n, theta_bins, table_1['ra'], table_1['dec'], RA2=table_r['ra'],
DEC2=table_r['dec'])['npairs']
d2r = DDtheta_mocks(
False, n, theta_bins, table_2['ra'], table_2['dec'], RA2=table_r['ra'],
DEC2=table_r['dec'])['npairs']
rr = DDtheta_mocks(
True, 4, theta_bins, table_r['ra'], table_r['dec'])['npairs']
return convert_3d_counts_to_cf(n_1, n_2, n_r, n_r, d1d2, d1r, d2r, rr)
def angular_corr_from_coords(ras,
decs,
randras,
randdecs,
weights=None,
randweights=None,
nthreads=1,
nbins=10):
bins = np.logspace(-2, 1, (nbins + 1))
# autocorrelation of catalog
DD_counts = DDtheta_mocks(1, nthreads, bins, ras, decs, weights1=weights)
# cross correlation between data and random catalog
DR_counts = DDtheta_mocks(0,
nthreads,
bins,
ras,
decs,
RA2=randras,
DEC2=randdecs,
weights1=weights,
weights2=randweights)
# autocorrelation of random points
RR_counts = DDtheta_mocks(1,
nthreads,
bins,
randras,
randdecs,
weights1=randweights)
wtheta = convert_3d_counts_to_cf(len(ras), len(ras), len(randras),
len(randras), DD_counts, DR_counts,
DR_counts, RR_counts)
return wtheta
if test:
mask_tar = select_patch([targets['RA'], targets['DEC']], limits=limits)
mask_ran = select_patch([randoms['RA'], randoms['DEC']], limits=limits)
else:
mask_tar = np.ones(len(targets['RA']), dtype=bool)
mask_ran = np.ones(len(randoms['RA']), dtype=bool)
for key, val in colours.items():
keep_tar = (mask_tar) & (val)
RA1, DEC1 = targets['RA'][keep_tar], targets['DEC'][keep_tar]
RAr, DECr = randoms['RA'][mask_ran], randoms['DEC'][mask_ran]
start = time.time()
pairs['DD_%s' % (key)] = DDtheta_mocks(1, nthreads, bins, RA1, DEC1)
end = time.time()
print('DD run time: %f sec' % (end - start))
start = time.time()
pairs['DR_%s' % (key)] = DDtheta_mocks(0,
nthreads,
bins,
RAr,
DECr,
RA2=RA1,
DEC2=DEC1)
end = time.time()
print('DR run time: %f sec' % (end - start))
np.save(
def select_patch(cat, limits):
patch = np.ones(len(cat[0]), bool)
patch &= np.logical_and(cat[0] > limits[0], cat[0] < limits[1])
patch &= np.logical_and(cat[1] > limits[2], cat[1] < limits[3])
return patch
#
test = False
nbins = 40
nthreads = 32
bins = np.logspace(np.log10(0.001), np.log10(10.0), nbins + 1) #log bins
if test:
limits = [160, 170, 5, 7]
mask_ran = select_patch([randoms['RA'], randoms['DEC']], limits=limits)
RAr, DECr = randoms['RA'][mask_ran], randoms['DEC'][mask_ran]
else:
RAr, DECr = randoms['RA'], randoms['DEC']
start = time.time()
RR = DDtheta_mocks(0, nthreads, bins, RAr, DECr, RA2=RAr, DEC2=DECr)
end = time.time()
print('RR run time: %f sec' % (end - start))
np.save(
'/global/cscratch1/sd/qmxp55/bgstargets_output/dr9/clustering/pair_counts/RR_counts_randoms1_south_nominal',
RR)
#split array in chunks
chunks = {}
n = 16
for i in range(n):
chunks['%i' % (i)] = idx[i::n]
#
ran_pairs = {}
nbins = 40
nthreads = 16
bins = np.logspace(np.log10(0.001), np.log10(10.0), nbins + 1) #log bins
for keyR, val in chunks.items():
RAr, DECr = randoms['RA'][val], randoms['DEC'][val]
start = time.time()
ran_pairs['RR_%s' % (keyR)] = DDtheta_mocks(0,
nthreads,
bins,
RAr,
DECr,
RA2=RAr,
DEC2=DECr)
end = time.time()
print('RR_%s run time: %f sec' % (keyR, end - start))
np.save(
'/global/cscratch1/sd/qmxp55/bgstargets_output/dr9/clustering/pair_counts/RR_south_16.npy',
ran_pairs)